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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Tkatek, Said
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
A hybrid approach using convolutional neural networks and genetic algorithm to improve of sensing brain tumor prediction Ettakifi, Hamza; Tkatek, Said
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4325-4335

Abstract

Brain tumor is the most constantly diagnosed cancer, and the opinion of the brain is veritably sensitive and complex, which is the subject of numerous studies and inquiries. In computer vision, deep literacy ways, similar as the convolutional neural network (CNN), are employed due to their bracket capabilities using learned point styles and their capability to work with complex images. still, their performance is largely dependent on the network structure and the named optimization system for tuning the network parameters. In this paper, we present new yet effective styles for training convolutional neural networks. The maturity of current state-of-the-art literacy styles for convolutional neural networks are grounded on grade descent. In discrepancy to traditional convolutional neural network training styles, we propose an enhancement by incorporating the inheritable algorithm for brain tumor prediction. Our work involves designing a convolutional neural network model to grease the bracket process, training the model using different optimizers (Adam and the inheritable algorithm), and assessing the model through colorful trials on the brain magnetic resonance imaging (MRI) dataset. We demonstrate that the convolutional neural network model trained using the inheritable algorithm performs as well as the Adam optimizer, achieving a bracket delicacy of 99.5.
Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience Idouglid, Lahcen; Tkatek, Said; Elfayq, Khalid; Guezzaz, Azidine
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3512-3521

Abstract

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.
Predicting television programs success using machine learning techniques Fayq, Khalid El; Tkatek, Said; Idouglid, Lahcen
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5502-5512

Abstract

In the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models' efficacy, revealing a standout performer with a mean absolute percent error of just under 8%. Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels.
Co-Authors El Fayq, Khalid Elfayq, Khalid Ettakifi, Hamza Fayq, Khalid El Guezzaz, Azidine Idouglid, Lahcen